Open-source Bloomreach alternatives

Vespa is a platform for applications that must pick a subset of data from a large, changing corpus, evaluate machine-learned models over it, organize and aggregate the results and return them quickly. Typical examples are search, recommendation and personalization, and the platform adds vector search and retrieval-augmented generation as well, as shown in its topics.

Doing this over large data sets distributed across many nodes and evaluated in parallel is hard, and Vespa handles it with high availability and performance. It handles searching, running inference over and organizing vectors, tensors, text and structured data while serving. According to the README, Vespa has been developed over many years and runs behind several large internet services.

The repository contains all the code needed to build and run Vespa yourself under the Apache 2.0 licence, and new releases are made from the master branch on weekday mornings. You can deploy applications to the Vespa Cloud service, which offers a free trial, or run your own instance following the getting started guide. It is written in Java and C++ and suits teams building large-scale search and recommendation systems.

Marqo builds AI-native search and product discovery for online brands in categories such as fashion, beauty, electronics and home goods. Using semantic search and personalization, it learns from browsing clicks, orders and other events to infer what shoppers want and return relevant search results and recommendations.

The vendor says its platform helps commerce teams improve search relevance, raise conversion and order value, and reduce manual merchandising through automated ranking and optimization. The repository also hosts the open-source search engine project, with topics covering multi-modal search and machine learning. A notice at the top of the README clarifies the status of the open-source project relative to the commercial platform, so read it before adopting the code.

The code is written in Python under the Apache-2.0 license. You can run the open-source engine yourself, or buy the managed ecommerce platform from the vendor. It suits online retailers and developers evaluating vector search for product catalogs.

ElasticSuite, from Smile, is a search and merchandising module for Magento 2 built on Elasticsearch. It is installed into a Magento store to power catalog search, autocomplete and faceted navigation, and to give merchandisers tools for controlling how products are ranked and displayed.

The project maintains a compatibility matrix because Magento 2.4.6 introduced Elasticsearch 8 and OpenSearch 2: different ElasticSuite releases line up with different Magento versions, from the 2.9.x line for older stores up to 2.12.x for the newest, and with Elasticsearch 7 and 8 or OpenSearch 1 to 3. Stores using Magento Commerce B2B features need separate Shared Catalog and Quick Order modules.

A set of free additional modules is also published on GitHub, including CMS page search, rating filters and sorting, a retail suite with a store locator and per-store price segmentation, and target rules computed through Elasticsearch. ElasticSuite is written in PHP and licensed under OSL-3.0, and it needs your own Magento and search cluster.